Agentic AI Engineer Co-Op

An Agentic AI Engineer designs, builds, and deploys autonomous AI systems that can reason, plan, use tools, and execute multi-step workflows with minimal human intervention . Unlike traditional AI/ML engineers who focus on model training and prediction, agentic AI engineers orchestrate goal-driven workflows that integrate models, tools, memory, and business logic to achieve objectives dynamically

Core Responsibilities

•Design & Develop Agentic Systems: Build intelligent agents capable of autonomous planning, reasoning, and task execution, often using LLMs (e.g., GPT-class, LLaMA), multi-modal models, and autonomous workflows

•Orchestration & Frameworks: Implement agent orchestration using frameworks like LangChain, AutoGen, CrewAI, Semantic Kernel, or custom solutions

•Retrieval-Augmented Generation (RAG): Design and optimize RAG pipelines for enhanced reasoning with external knowledge, including document ingestion, chunking, embeddings, vector stores, and retrieval ranking

•Tool & Memory Integration: Develop agents that call APIs, databases, and other tools, maintain memory, and adapt based on outcomes

•Evaluation & Monitoring: Create evaluation frameworks for accuracy, grounding, latency, and cost; build observability for agent behavior and failure modes

•Model Adaptation: Fine-tune or adapt foundation models (e.g., via LoRA, adapters) for domain-specific use cases

•Production Deployment: Deploy GenAI/agentic systems in cloud-native environments with CI/CD, versioning, and runtime safeguards

•Cross-Functional Collaboration: Work with data scientists, ML engineers, product teams, and governance/compliance stakeholders

Required Skills & Experience

•2+ years in AI/ML system design, deployment, or autonomous agent development

•Programming: Proficiency in Python (and sometimes Java, C#) for AI/ML solution development

•Agent & Workflow Expertise: Experience with agent orchestration frameworks and multi-agent communication protocols

•RAG & LLM Integration: Hands-on with RAG architectures, evaluation methodologies, and LLM integration

•Cloud & DevOps: Experience with cloud platforms (e.g., Azure, AWS) and CI/CD pipelines

•Governance & Compliance: Understanding of responsible AI, security, and compliance in regulated domains (e.g., retail)